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Search Results (1,508)

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Keywords = long-term ART

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18 pages, 330 KiB  
Essay
Music and Arts in Early Childhood Education: Paths for Professional Development Towards Social and Human Development
by Helena Rodrigues, Ana Isabel Pereira, Paulo Maria Rodrigues, Paulo Ferreira Rodrigues and Angelita Broock
Educ. Sci. 2025, 15(8), 991; https://doi.org/10.3390/educsci15080991 (registering DOI) - 4 Aug 2025
Abstract
This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a [...] Read more.
This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a transdisciplinary approach. Based on initiatives promoted by the Companhia de Música Teatral (CMT) and the Education and Human Development Group of the Centre for the Study of Sociology and Musical Aesthetics (CESEM) at NOVA University Lisbon, the article highlights projects such as: (i) Opus Tutti and GermInArte, developed between 2011 and 2018; (ii) the Postgraduate Course Music in Childhood: Intervention and Research, offered at the University since 2020/21, which integrates art, health, and education, promoting collaborative work between professionals; and (iii) Mil Pássaros (Thousand Birds), developed since 2020, which exemplifies the integration of environmental education and artistic practices. The theoretical basis of these training programs combines neuroscientific and educational evidence, emphasizing the importance of the first years of life for integral development. Studies, such as those by Heckman, reinforce the impact of early investment in children’s development. Edwin Gordon’s Music Learning Theory and Malloch and Trevarthen’s concept of ‘communicative musicality’ structure the design of these courses, recognizing music as a catalyst for cognitive, emotional, and social skills. The transformative role of music and the arts in educational and social contexts is emphasized, in line with the Sustainable Development Goals of the 2030 Agenda, by proposing approaches that articulate creation, intervention, and research to promote human development from childhood onwards. Full article
14 pages, 533 KiB  
Article
Immunorecovered but Exhausted: Persistent PD-1/PD-L1 Expression Despite Virologic Suppression and CD4 Recovery in PLWH
by Bogusz Aksak-Wąs, Karolina Skonieczna-Żydecka, Miłosz Parczewski, Rafał Hrynkiewicz, Filip Lewandowski, Karol Serwin, Kaja Mielczak, Adam Majchrzak, Mateusz Bruss and Paulina Niedźwiedzka-Rystwej
Biomedicines 2025, 13(8), 1885; https://doi.org/10.3390/biomedicines13081885 - 3 Aug 2025
Viewed by 78
Abstract
Background/Objectives: While ART effectively suppresses HIV viremia, many PLWH exhibit persistent immune dysfunction. This study aimed to assess immune recovery and immune exhaustion (PD-1/PD-L1 expression) in newly diagnosed versus long-term ART-treated individuals. Methods: We analyzed 79 PLWH: 52 newly diagnosed individuals (12-month follow-up) [...] Read more.
Background/Objectives: While ART effectively suppresses HIV viremia, many PLWH exhibit persistent immune dysfunction. This study aimed to assess immune recovery and immune exhaustion (PD-1/PD-L1 expression) in newly diagnosed versus long-term ART-treated individuals. Methods: We analyzed 79 PLWH: 52 newly diagnosed individuals (12-month follow-up) and 27 long-term-treated patients (Ukrainian refugees). Flow cytometry was used to evaluate CD4+ and CD8+ counts, the CD4+/CD8+ ratio, and PD-1/PD-L1 expression on CD3+, CD4+, and CD19+ lymphocytes. ART regimen and HIV subtype were included as covariates in linear regression models. Results: At 12 months, CD4+ counts were similar between groups (median 596.5 vs. 621 cells/μL, p = 0.22), but newly diagnosed patients had higher CD8+ counts (872 vs. 620 cells/μL, p = 0.028) and a lower CD4+/CD8+ ratio (0.57 vs. 1.05, p = 0.0027). Immune exhaustion markers were significantly elevated in newly diagnosed individuals: CD4+ PD-1+ T cells (24.4% vs. 3.85%, p = 0.0002) and CD3+ PD-1+ T cells (27.3% vs. 12.35%, p < 0.0001). Linear regression confirmed group membership independently predicted higher CD3+ (β = +21.92, p < 0.001), CD4+ (β = +28.87, p < 0.0001), and CD19+ (β = +8.73, p = 0.002) percentages. Lipid parameters and SCORE2 did not differ significantly. Conclusions: Despite virologic suppression and CD4+ recovery, immune exhaustion markers remain elevated in newly diagnosed PLWH, suggesting incomplete immune normalization. Traditional parameters (CD4+ count and CD4+/CD8+ ratio) may not fully capture immune status, warranting broader immunologic profiling in HIV care. Full article
(This article belongs to the Special Issue Pathogenesis, Diagnosis and Treatment of Infectious Diseases)
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20 pages, 10013 KiB  
Article
Addressing Challenges in Rds,on Measurement for Cloud-Connected Condition Monitoring in WBG Power Converter Applications
by Farzad Hosseinabadi, Sachin Kumar Bhoi, Hakan Polat, Sajib Chakraborty and Omar Hegazy
Electronics 2025, 14(15), 3093; https://doi.org/10.3390/electronics14153093 - 2 Aug 2025
Viewed by 102
Abstract
This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (Rds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, [...] Read more.
This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (Rds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and Rds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article
(This article belongs to the Section Industrial Electronics)
30 pages, 1142 KiB  
Review
Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting
by He Huang, Difei Deng, Liang Hu, Yawen Chen and Nan Sun
Remote Sens. 2025, 17(15), 2675; https://doi.org/10.3390/rs17152675 - 2 Aug 2025
Viewed by 119
Abstract
Accurate forecasting of tropical cyclone (TC) tracks is critical for disaster preparedness and risk mitigation. While traditional numerical weather prediction (NWP) systems have long served as the backbone of operational forecasting, they face limitations in computational cost and sensitivity to initial conditions. In [...] Read more.
Accurate forecasting of tropical cyclone (TC) tracks is critical for disaster preparedness and risk mitigation. While traditional numerical weather prediction (NWP) systems have long served as the backbone of operational forecasting, they face limitations in computational cost and sensitivity to initial conditions. In recent years, deep learning (DL) has emerged as a promising alternative, offering data-driven modeling capabilities for capturing nonlinear spatiotemporal patterns. This paper presents a comprehensive review of DL-based approaches for TC track forecasting. We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. To enable rigorous performance comparison, we introduce a Unified Geodesic Distance Error (UGDE) metric that standardizes evaluation across diverse studies and lead times. Based on this metric, we conduct a critical comparison of state-of-the-art models and identify key insights into their relative strengths, limitations, and suitable application scenarios. Building on this framework, we conduct a critical cross-model analysis that reveals key trends, performance disparities, and architectural tradeoffs. Our analysis also highlights several persistent challenges, such as long-term forecast degradation, limited physical integration, and generalization to extreme events, pointing toward future directions for developing more robust and operationally viable DL models for TC track forecasting. To support reproducibility and facilitate standardized evaluation, we release an open-source UGDE conversion tool on GitHub. Full article
(This article belongs to the Section AI Remote Sensing)
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19 pages, 523 KiB  
Review
Whey Proteins and Metabolic Dysfunction-Associated Steatotic Liver Disease Features: Evolving the Current Knowledge and Future Trends
by Maja Milanović, Nataša Milošević, Maja Ružić, Ludovico Abenavoli and Nataša Milić
Metabolites 2025, 15(8), 516; https://doi.org/10.3390/metabo15080516 - 1 Aug 2025
Viewed by 310
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is a prevalent, multisystem disease affecting approximately 30% of adults worldwide. Obesity, along with dyslipidemia, type 2 diabetes mellitus, and hypertension, are closely intertwined with MASLD. In people with [...] Read more.
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is a prevalent, multisystem disease affecting approximately 30% of adults worldwide. Obesity, along with dyslipidemia, type 2 diabetes mellitus, and hypertension, are closely intertwined with MASLD. In people with obesity, MASLD prevalence is estimated to be about 75%. Despite various approaches to MASLD treatment, dietary changes remain the most accessible and safe interventions in MASLD, especially in obese and overweight patients. Whey proteins are rich in bioactive compounds, essential amino acids with antioxidant properties, offering potential benefits for MASLD prevention and management. This state-of-the-art review summarizes whey protein impacts on a spectrum of MASLD-related manifestations, such as obesity, impaired glucose and lipid metabolism, hypertension, liver injury, oxidative stress, and inflammation. The results obtained in clinical environments, with a focus on meta-analysis, propose whey protein supplementation as a promising strategy aimed at managing multifaced MASLD disorders. Well-designed cohort studies are needed for validation of the efficacy and long-term safety of whey proteins in MASLD patients. Full article
(This article belongs to the Special Issue Effects of Diet on Metabolic Health of Obese People)
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26 pages, 8736 KiB  
Article
Uncertainty-Aware Fault Diagnosis of Rotating Compressors Using Dual-Graph Attention Networks
by Seungjoo Lee, YoungSeok Kim, Hyun-Jun Choi and Bongjun Ji
Machines 2025, 13(8), 673; https://doi.org/10.3390/machines13080673 - 1 Aug 2025
Viewed by 219
Abstract
Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a [...] Read more.
Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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30 pages, 4409 KiB  
Article
Accident Impact Prediction Based on a Deep Convolutional and Recurrent Neural Network Model
by Pouyan Sajadi, Mahya Qorbani, Sobhan Moosavi and Erfan Hassannayebi
Urban Sci. 2025, 9(8), 299; https://doi.org/10.3390/urbansci9080299 - 1 Aug 2025
Viewed by 260
Abstract
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of the real-time forecasting of post-accident impact using readily available data can play a crucial role [...] Read more.
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of the real-time forecasting of post-accident impact using readily available data can play a crucial role in preventing adverse outcomes and enhancing overall safety. However, existing accident predictive models encounter two main challenges: first, a reliance on either costly or non-real-time data, and second, the absence of a comprehensive metric to measure post-accident impact accurately. To address these limitations, this study proposes a deep neural network model known as the cascade model. It leverages readily available real-world data from Los Angeles County to predict post-accident impacts. The model consists of two components: Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). The LSTM model captures temporal patterns, while the CNN extracts patterns from the sparse accident dataset. Furthermore, an external traffic congestion dataset is incorporated to derive a new feature called the “accident impact” factor, which quantifies the influence of an accident on surrounding traffic flow. Extensive experiments were conducted to demonstrate the effectiveness of the proposed hybrid machine learning method in predicting the post-accident impact compared to state-of-the-art baselines. The results reveal a higher precision in predicting minimal impacts (i.e., cases with no reported accidents) and a higher recall in predicting more significant impacts (i.e., cases with reported accidents). Full article
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28 pages, 5699 KiB  
Article
Multi-Modal Excavator Activity Recognition Using Two-Stream CNN-LSTM with RGB and Point Cloud Inputs
by Hyuk Soo Cho, Kamran Latif, Abubakar Sharafat and Jongwon Seo
Appl. Sci. 2025, 15(15), 8505; https://doi.org/10.3390/app15158505 (registering DOI) - 31 Jul 2025
Viewed by 128
Abstract
Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving [...] Read more.
Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article
(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)
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21 pages, 6892 KiB  
Article
Enhanced Temporal Action Localization with Separated Bidirectional Mamba and Boundary Correction Strategy
by Xiangbin Liu and Qian Peng
Mathematics 2025, 13(15), 2458; https://doi.org/10.3390/math13152458 - 30 Jul 2025
Viewed by 232
Abstract
Temporal action localization (TAL) is a research hotspot in video understanding, which aims to locate and classify actions in videos. However, existing methods have difficulties in capturing long-term actions due to focusing on local temporal information, which leads to poor performance in localizing [...] Read more.
Temporal action localization (TAL) is a research hotspot in video understanding, which aims to locate and classify actions in videos. However, existing methods have difficulties in capturing long-term actions due to focusing on local temporal information, which leads to poor performance in localizing long-term temporal sequences. In addition, most methods ignore the boundary importance for action instances, resulting in inaccurate localized boundaries. To address these issues, this paper proposes a state space model for temporal action localization, called Separated Bidirectional Mamba (SBM), which innovatively understands frame changes from the perspective of state transformation. It adapts to different sequence lengths and incorporates state information from the forward and backward for each frame through forward Mamba and backward Mamba to obtain more comprehensive action representations, enhancing modeling capabilities for long-term temporal sequences. Moreover, this paper designs a Boundary Correction Strategy (BCS). It calculates the contribution of each frame to action instances based on the pre-localized results, then adjusts weights of frames in boundary regression to ensure the boundaries are shifted towards the frames with higher contributions, leading to more accurate boundaries. To demonstrate the effectiveness of the proposed method, this paper reports mean Average Precision (mAP) under temporal Intersection over Union (tIoU) thresholds on four challenging benchmarks: THUMOS13, ActivityNet-1.3, HACS, and FineAction, where the proposed method achieves mAPs of 73.7%, 42.0%, 45.2%, and 29.1%, respectively, surpassing the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advances in Applied Mathematics in Computer Vision)
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12 pages, 433 KiB  
Article
Cardiac Function in Women with and Without Previous Assisted Reproductive Technology: A Prospective Observational Cohort Study
by Freya Baird, Eleni Kakouri, Iulia Huluta, Ippokratis Sarris, Sesh K. Sunkara, Kypros H. Nicolaides and Nick Kametas
J. Clin. Med. 2025, 14(15), 5366; https://doi.org/10.3390/jcm14155366 - 29 Jul 2025
Viewed by 309
Abstract
Background: Previous research has linked hypertensive disorders of pregnancy (HDP) and long-term cardiovascular disease (CVD) with assisted reproductive technology (ART). It is not clear whether this reflects the background population cardiovascular profiles or whether ART independently increases the long-term risk for CVD [...] Read more.
Background: Previous research has linked hypertensive disorders of pregnancy (HDP) and long-term cardiovascular disease (CVD) with assisted reproductive technology (ART). It is not clear whether this reflects the background population cardiovascular profiles or whether ART independently increases the long-term risk for CVD and alters cardiovascular function. Furthermore, CVD has been associated with pathological cardiovascular function before and after the establishment of the disease. The aim of this study was to compare cardiac function in women attending for ART between those who had previous treatment and those who had not after controlling for demographic characteristics which have been shown to affect cardiovascular function. Methods: This was a prospective observational cohort study at a London fertility clinic. Women were consecutively enrolled between May 2021 and March 2022. Maternal demographics and cardiac function using transthoracic echocardiography were assessed before the current treatment cycle in the mid-luteal phase of the menstrual cycle. Maternal demographics included age, body mass index, smoking, race, and parity. Cardiovascular parameters included blood pressure and indices of left-ventricular systolic and diastolic function. Differences between cardiac variables after controlling for maternal demographics and history of previous ART were assessed by multivariate linear regression. Results: There were 232 healthy women who agreed to participate in the study; of those, 153 (58%) had undergone previous ART. After controlling for maternal demographic characteristics, previous assisted reproductive technology was not an independent predictor of cardiac function. Conclusions: Previous ART is not associated with significant changes in cardiac function. Full article
(This article belongs to the Section Obstetrics & Gynecology)
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13 pages, 986 KiB  
Article
Enhanced Cross-Audiovisual Perception in High-Level Martial Arts Routine Athletes Stems from Increased Automatic Processing Capacity
by Xiaohan Wang, Zeshuai Wang, Ya Gao, Wu Jiang, Zikang Meng, Tianxin Gu, Zonghao Zhang, Haoping Yang and Li Luo
Behav. Sci. 2025, 15(8), 1028; https://doi.org/10.3390/bs15081028 - 29 Jul 2025
Viewed by 181
Abstract
Multisensory integration is crucial for effective cognitive functioning, especially in complex tasks such as those requiring rapid audiovisual information processing. High-level martial arts routine athletes, trained in integrating visual and auditory cues for performance, may exhibit superior abilities in cross-audiovisual integration. This study [...] Read more.
Multisensory integration is crucial for effective cognitive functioning, especially in complex tasks such as those requiring rapid audiovisual information processing. High-level martial arts routine athletes, trained in integrating visual and auditory cues for performance, may exhibit superior abilities in cross-audiovisual integration. This study aimed to explore whether these athletes demonstrate an expert advantage effect in audiovisual integration, particularly focusing on whether this advantage is due to enhanced automatic auditory processing. A total of 165 participants (81 male, 84 female) were included in three experiments. Experiment 1 (n = 63) used a cross-audiovisual Rapid Serial Visual Presentation (RSVP) paradigm to compare the martial arts routine athlete group (n = 31) with a control group (n = 33) in tasks requiring target stimulus identification under audiovisual congruent and incongruent conditions. Experiment 2 (n = 52) manipulated the synchronicity of auditory stimuli to differentiate between audiovisual integration and auditory alerting effects. Experiment 3 (n = 50) combined surprise and post-surprise tests to investigate the role of automatic auditory processing in this expert advantage. Experiment 1 revealed that martial arts routine athletes outperformed the control group, especially in semantically incongruent conditions, with significantly higher accuracy at both lag3 (p < 0.001, 95% CI = [0.165, 0.275]) and lag8 (p < 0.001, 95% CI = [0.242, 0.435]). Experiment 2 found no significant difference between groups in response to the manipulation of auditory stimulus synchronicity, ruling out an alerting effect. In Experiment 3, martial arts routine athletes demonstrated better performance in reporting unexpected auditory stimuli during the surprise test, indicating enhanced automatic processing capacity. Additionally, a significant improvement in working memory re-selection was observed in the martial arts routine group. The expert advantage effect observed in martial arts routine athletes is attributable to enhanced cross-audiovisual integration, independent of an auditory alerting mechanism. Long-term training improves the efficiency of working memory re-selection and the ability to inhibit conflicting information, suggesting that the expanded capacity for automatic auditory processing underpins their multisensory integration advantage. Full article
(This article belongs to the Section Cognition)
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15 pages, 2095 KiB  
Article
T-Lymphocyte Phenotypic and Mitochondrial Parameters as Markers of Incomplete Immune Restoration in People Living with HIV+ on Long-Term cART
by Damian Vangelov, Radoslava Emilova, Yana Todorova, Nina Yancheva, Reneta Dimitrova, Lyubomira Grigorova, Ivailo Alexiev and Maria Nikolova
Biomedicines 2025, 13(8), 1839; https://doi.org/10.3390/biomedicines13081839 - 28 Jul 2025
Viewed by 436
Abstract
Background/Objectives: Restored CD4 absolute counts (CD4AC) and CD4/CD8 ratio in the setting of continuous antiretroviral treatment (ART) do not exclude a low-level immune activation associated with HIV reservoirs, microbial translocation, or the side effects of ART itself, which accelerates the aging of [...] Read more.
Background/Objectives: Restored CD4 absolute counts (CD4AC) and CD4/CD8 ratio in the setting of continuous antiretroviral treatment (ART) do not exclude a low-level immune activation associated with HIV reservoirs, microbial translocation, or the side effects of ART itself, which accelerates the aging of people living with HIV (PLHIV). To delineate biomarkers of incomplete immune restoration in PLHIV on successful ART, we evaluated T-lymphocyte mitochondrial parameters in relation to phenotypic markers of immune exhaustion and senescence. Methods: PLHIV with sustained viral suppression, CD4AC > 500 and CD4/CD8 ratio >0.9 on ART (n = 39) were compared to age-matched ART-naïve donors (n = 27) and HIV(–) healthy controls (HC, n = 35). CD4 and CD8 differentiation and effector subsets (CCR7/CD45RA and CD27/CD28), activation, exhaustion, and senescence markers (CD38, CD39 Treg, CD57, TIGIT, and PD-1) were determined by flow cytometry. Mitochondrial mass (MM) and membrane potential (MMP) of CD8 and CD4 T cells were evaluated with MitoTracker Green and Red flow cytometry dyes. Results: ART+PLHIV differed from HC by increased CD4 TEMRA (5.3 (2.1–8.8) vs. 3.2 (1.6–4.4), p < 0.05), persistent TIGIT+CD57–CD27+CD28– CD8+ subset (53.9 (45.5–68.9) vs. 40.1 (26.7–58.5), p < 0.05), and expanding preapoptotic TIGIT–CD57+CD8+ effectors (9.2 (4.3–21.8) vs. 3.0 (1.5–7.3), p < 0.01) in correlation with increased CD8+ MMP (2527 (1675–4080) vs.1477 (1280–1691), p < 0.01). These aberrations were independent of age, time to ART, or ART duration, and were combined with increasing CD4 T cell MMP and MM. Conclusions: In spite of recovered CD4AC and CD4/CD8 ratio, the increased CD8+ MMP, combined with elevated markers of exhaustion and senescence in ART+PLHIV, signals a malfunction of the CD8 effector pool that may compromise viral reservoir latency. Full article
(This article belongs to the Special Issue Emerging Insights into HIV)
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13 pages, 3512 KiB  
Article
Cumulative Risk for Periprosthetic Fracture and Operative Treatment Options After Revision Total Hip Arthroplasty with a Modular and Tapered Revision Device—A Consecutive Series of 117 Cases in a Mid-Term Duration
by Oliver E. Bischel, Matthias K. Jung, Max Pilgrim, Arnold J. Höppchen, Paul M. Böhm and Jörn B. Seeger
J. Clin. Med. 2025, 14(15), 5321; https://doi.org/10.3390/jcm14155321 - 28 Jul 2025
Viewed by 260
Abstract
Background: Implantation of modularly built-up stems with a tapered and fluted design is currently state of the art in revision total hip arthroplasty (RTHA). Nevertheless, implant-specific major complications like breakage of taper junctions as well as periprosthetic fractures (PPFs) may lead to failure [...] Read more.
Background: Implantation of modularly built-up stems with a tapered and fluted design is currently state of the art in revision total hip arthroplasty (RTHA). Nevertheless, implant-specific major complications like breakage of taper junctions as well as periprosthetic fractures (PPFs) may lead to failure of reconstruction during follow-up. Methods: A cohort of 117 cases receiving femoral RTHA by a modular stem was investigated retrospectively with a mean follow-up of 5.7 (0.5–13.7) years. Cumulative risk and potential factors affecting the occurrence of PPFs were calculated with the Kaplan–Meier method. In addition, cases were presented to discuss operative treatment options. Results: A cumulative risk of PPF of 12.1% (95% CI: 0–24.6%) was calculated at 13.7 years. Female patients had significantly higher risk compared to male patients (0% after 13.5 years for male patients vs. 20.8% (95% CI: 0.5–41.2%) after 13.7 years for female patients; log-rank p = 0.0438) as all five patients sustaining a PPF during follow-up were women. Four fractures were treated by open reduction and internal fixation. Non-union and collapse of the fracture occurred in one patient after closed reduction and internal fixation. Conclusions: Postoperative PPF after femoral revision with a modular stem has shown to be a frequent complication within this mid-term follow-up. Female patients were at a significantly higher risk in this aged cohort, indicating osteoporosis as a risk factor. The surgical treatment of PPF with an integrated long-stemmed prosthesis is challenging and thorough considerations of adequate operative treatment of PPFs are strongly advised in order to limit complication rates. Full article
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23 pages, 13580 KiB  
Article
Enabling Smart Grid Resilience with Deep Learning-Based Battery Health Prediction in EV Fleets
by Muhammed Cavus and Margaret Bell
Batteries 2025, 11(8), 283; https://doi.org/10.3390/batteries11080283 - 24 Jul 2025
Viewed by 270
Abstract
The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful [...] Read more.
The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, R2: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article
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23 pages, 3741 KiB  
Article
Multi-Corpus Benchmarking of CNN and LSTM Models for Speaker Gender and Age Profiling
by Jorge Jorrin-Coz, Mariko Nakano, Hector Perez-Meana and Leobardo Hernandez-Gonzalez
Computation 2025, 13(8), 177; https://doi.org/10.3390/computation13080177 - 23 Jul 2025
Viewed by 280
Abstract
Speaker profiling systems are often evaluated on a single corpus, which complicates reliable comparison. We present a fully reproducible evaluation pipeline that trains Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) models independently on three speech corpora representing distinct recording conditions—studio-quality TIMIT, [...] Read more.
Speaker profiling systems are often evaluated on a single corpus, which complicates reliable comparison. We present a fully reproducible evaluation pipeline that trains Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) models independently on three speech corpora representing distinct recording conditions—studio-quality TIMIT, crowdsourced Mozilla Common Voice, and in-the-wild VoxCeleb1. All models share the same architecture, optimizer, and data preprocessing; no corpus-specific hyperparameter tuning is applied. We perform a detailed preprocessing and feature extraction procedure, evaluating multiple configurations and validating their applicability and effectiveness in improving the obtained results. A feature analysis shows that Mel spectrograms benefit CNNs, whereas Mel Frequency Cepstral Coefficients (MFCCs) suit LSTMs, and that the optimal Mel-bin count grows with corpus Signal Noise Rate (SNR). With this fixed recipe, EfficientNet achieves 99.82% gender accuracy on Common Voice (+1.25 pp over the previous best) and 98.86% on VoxCeleb1 (+0.57 pp). MobileNet attains 99.86% age-group accuracy on Common Voice (+2.86 pp) and a 5.35-year MAE for age estimation on TIMIT using a lightweight configuration. The consistent, near-state-of-the-art results across three acoustically diverse datasets substantiate the robustness and versatility of the proposed pipeline. Code and pre-trained weights are released to facilitate downstream research. Full article
(This article belongs to the Section Computational Engineering)
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